Krea

Models by this creator

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aesthetic-controlnet

krea

Total Score

69

Aesthetic ControlNet is a model that can produce highly aesthetic results from an input image and a text prompt. It uses a ControlNet, a method that can condition diffusion models on arbitrary input features, combined with a Canny edge detector and a fine-tuned version of Stable Diffusion 2.1 trained on a large aesthetic dataset. Similar models include sd-controlnet-canny which uses ControlNet conditioned on Canny edges, and controlnet-canny-sdxl-1.0 which uses ControlNet with Canny conditioning on the larger Stable Diffusion XL base model. Model inputs and outputs Inputs An input image, which is processed using a Canny edge detector to extract the edge features A text prompt describing the desired image Outputs A generated image that matches the text prompt, with aesthetic qualities guided by the Canny edge input Capabilities The Aesthetic ControlNet model can produce highly stylized and visually appealing images from text prompts. By conditioning the text-to-image generation on the edge features extracted from an input image, the model is able to generate images that closely match the aesthetic qualities of the provided image, while still adhering to the text prompt. What can I use it for? The Aesthetic ControlNet model can be used to create visually striking images for a variety of applications, such as digital art, product design, advertising, and more. The ability to control the aesthetic qualities of the generated images makes it a powerful tool for creative professionals who need to produce high-quality, on-brand visuals. Things to try One interesting thing to try with the Aesthetic ControlNet model is to experiment with different types of input images. While the examples provided use a Canny edge detector, you could also try using other image preprocessing techniques, such as depth maps or surface normals, to see how they affect the generated outputs. Additionally, you can play with the model's hyperparameters, such as the guidance scale and the number of inference steps, to find the optimal settings for your specific use case.

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Updated 5/17/2024